Erica Smith, VP, Business Development
According to a study published in Biostatistics, the estimated success rate of drugs entering clinical development is just 13.8%. Often, Phase II and III clinical trials fail due to the inability to demonstrate clear superiority of the tested therapy versus a placebo, which can lead to increased development costs, extended timelines, and even the premature abandonment of entire development programs, according to a study published in Nature.
Tools4Patient (T4P) developed Placebell to predict placebo response, which has challenged drug development for decades. To solve this issue, T4P combined machine learning with its proprietary Multidimension Psychological Questionnaire (MPsQ) that measures personality traits, expectations, and other factors. Using this information, each patient’s placebo responsiveness is calculated using a machine learning-based model, which can be used in statistical analysis to improve trial success with no risk.
In 2021, T4P achieved critical milestones demonstrating that the Placebell approach can predict placebo response in multiple diseases in sponsored studies, including chronic pain, Parkinson’s disease, and ophthalmology (dry eye disease). Additionally, more trials are ongoing in areas such as schizophrenia and autoimmune disease. Furthermore, new initiatives were kicked off in 2021 to develop the Placebell method in critical diseases such as Alzheimer’s disease and depression that have historically experienced very high clinical trial failure rates due in part to high placebo response rates.
In general, Placebell has been demonstrated to explain between 25% to 35% of data variability related to the placebo response across endpoints and indications, regardless of route of drug administration and study context.